This week’s highlighted open-source project is NeoML, a library for building, training, and deploying machine learning models.
According to ABBYY, the company that created NeoML, the project is best suited for applications running in cloud environments, on desktop, and on mobile devices. Developers can use the library for object identification, classification, semantic segmentation, verification, and predictive modeling.
An example use case is a bank using NeoML to develop models to manage credit risk and predict customer churn, ABBYY explained.
Currently NeoML supports C++, Java, and Objective-C. ABBYY plans on adding support for Python soon. The library also supports over 100 layer types, has more than 20 traditional machine learning algorithms, and is fully cross-platform.
In addition, NeoML supports the Open Neural Network Exchange (ONNX), which is an ecosystem for interoperable machine learning models. ONNX improves tool compatibility, which makes it easier for developers to use various tools to meet their goals, ABBYY explained.
“The launch of NeoML reflects our commitment to contribute to industry-wide AI innovation,” said Ivan Yamshchikov, AI Evangelist at ABBYY. “ABBYY has a proven track record of technological innovation with over 400 patents and patent applications. Sharing our framework allows developers to leverage its inference speed, cross-platform capabilities, and especially its potential on mobile devices, while their feedback and contribution will grow and improve the library. We are thrilled to promote advancements in AI and support machine learning being applied to increasingly high-value and impactful use cases.”
The project can be found on GitHub.
